Using SQL Market Data to Rank Volatility and Replay Crypto Ticks
Summary
The document introduces a platform data exploration tool for querying exchange OHLC and tick data with SQL, including user-uploaded datasets. It explains how query parameters can make filters adjustable, how results can be viewed as tables or visualizations, and how saved queries and shared views support repeatable research. Results can also be exported in common data formats.
Two examples show how the tool can support market analysis. One groups daily crypto futures data by symbol and ranks recent price ranges relative to average price, with parameters controlling the lookback, sort direction, and output size. The other retrieves recent Binance tick records for a selected symbol and presents them in a time-based replay with multiple charts, intended for examining market microstructure. These are demonstrations of data access and visualization rather than validated trading strategies: the document provides no performance evaluation, transaction cost analysis, or evidence that volatility rankings predict returns. Data availability and the platform's query and upload limits also bound what users can study.
Key ideas
- SQL queries can filter and aggregate exchange OHLC and tick data for exploratory research.
- A range-to-average-price calculation can rank futures symbols by recent realized price movement.
- Parameterized queries make symbols, lookback windows, sorting, and result limits adjustable.
- Tick replay and linked charts help inspect market behavior over time at a fine resolution.
- The examples demonstrate tooling and do not establish predictive value or trading profitability.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.